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Sunday, August 2, 2020 | History

1 edition of Jackknifing the Kaplan-Meier survival estimator for censored data found in the catalog.

Jackknifing the Kaplan-Meier survival estimator for censored data

Donald Paul Gaver

Jackknifing the Kaplan-Meier survival estimator for censored data

simulation results and asymptotic analysis

by Donald Paul Gaver

  • 113 Want to read
  • 38 Currently reading

Published by Naval Postgraduate School in Monterey, Calif .
Written in English

    Subjects:
  • Reliability,
  • Asymptotic theory,
  • Regression analysis,
  • Failure time data analysis,
  • Estimation theory,
  • Survival and emergency equipment

  • About the Edition

    The Kaplan-Meier estimate is a non-parametric maximum likelihood estimate for the probability of equipment of human survival. This report describes a jackknife confidence limit procedure for probability of survival, based on K.-M., and describes confidence limit properties by simulation and by asymptotic analysis. (Author)

    Edition Notes

    Statementby D.P. Gaver and R.G. Miller
    SeriesNPS-55-82-004
    ContributionsMiller, R. G., Naval Postgraduate School (U.S.)
    The Physical Object
    Pagination29 p. ;
    Number of Pages29
    ID Numbers
    Open LibraryOL25453337M
    OCLC/WorldCa80562494

    Figure 3 – Kaplan-Meier Survival Analysis. The analysis for Example 3 is done similarly. This time by inserting A5:B23 in Input Range 1 and D5:E23 in Input Range 2 of Figure 2. The output is shown in Figure 4 and 5. Figure 4 – Kaplan-Meier Survival Analysis Part 1. Figure 5 – Kaplan-Meier Survival . Support multiple data input formats. Users can easily get hazards and survival functions which can be piped into visualziaiton or further data processing. Installation. kmsurvival can be installed with the following command: pip install kmsurvival Examples.

    The kaplan meier estimate in survival analysis. Biom Biostat Int J. ;5(2)‒ DOI: /bbij Censoring can occur when the patients lost to follow up to the end of the study. Censored data are data that arises when a person’s life length is known to happen only in a specified period of Size: KB.   As we have observed,1 analysis of survival data requires special techniques because some observations are censored as the event of interest has not occurred for all patients. For example, when patients are recruited over two years one recruited at the end of the study may be alive at one year follow up, whereas one recruited at the start may have died after two by:

    The Kaplan–Meier estimator is one of the most frequently used methods of survival analysis. The estimate may be useful to examine recovery rates, the probability of death, and the effectiveness of treatment.   Background. Although Kaplan-Meier survival analysis is commonly used to estimate the cumulative incidence of revision after joint arthroplasty, it theoretically overestimates the risk of revision in the presence of competing risks (such as death).Cited by:


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Jackknifing the Kaplan-Meier survival estimator for censored data by Donald Paul Gaver Download PDF EPUB FB2

It is shown by simulation t h a t the (arc-sine transformation of the) Kaplan-Meier survival estimator for censored data can be usefully jackknifed to give conservative confidence limits for survival probabilities when samples are small (25 and 50).

Mathematical demonstration of the asymptotic, large-sample, validity of the jackknife is by: ,sincethejackknife technique has beenshown tobe widelyuseful for obtainingrobust intervals, cf.

Miller (),it is applied to the Kaplan-Meier. texts All Books All Texts latest This Just In Smithsonian Libraries FEDLINK (US) Genealogy Lincoln Collection. National Emergency Library. Top American Libraries Canadian Libraries Universal Library Community Texts Project Gutenberg Biodiversity Heritage Library Children's Library.

Open Pages: For reducing bias of functionals of the Kaplan–Meier survival estimator, the jackknifing approach as proposed by Stute and Wang is a natural choice. We have extended their jackknifing approach to cover the case where the largest observation is censored by using different imputation approaches of the largest censored observations as proposed by Khan and by: 2.

The Kaplan-Meier estimate is a non-parametric maximum likelihood estimate for the probability of equipment of human survival. This report describes a jackknife confidence limit procedure for probability of survival, based on K.-M., and describes confidence limit properties by simulation and by asymptotic : Donald Paul Gaver and R.

Miller. Abstract For studying or reducing the bias of functionals of the Kaplan–Meier survival estimator, the jackknifing approach of Stute and Wang () is natural. We have studied the behavior of the jackknife estimate of bias under different configurations of the censoring level, sample size, and the censoring and survival time Size: KB.

Our second result, Theoremprovides an exact formula for the delete-2 jackknife estimate. The delete-2 jackknife estimate of a Kaplan-Meier integral Lemma below, which is the generalizations of Lemma 1 given by Stute and Wang (), will be required for deriving an explicit formula for the delete-2 jackknife of a Kaplan-Meier by: 2.

The jackknife estimate of variance of a Kaplan-Meier integral Stute, Winfried, The Annals of Statistics, ; Confidence Bands for the Kaplan-Meier Survival Curve Estimate Gillespie, Mary Jo and Fisher, Lloyd, The Annals of Statistics, ; Asymptotic Properties of Censored Linear Rank Tests Cuzick, Jack, The Annals of Statistics, ; Almost Sure Asymptotic Representation for a Class of.

For almost four decades the Kaplan–Meier estimator has been one of the key sta- tistical methods for analyzing censored survival data, and it is discussed in most textbooks on survival anal- ysis. Rigorous derivations of the statistical properties of the estimator are provided in the books by Fleming & Harrington and Andersen et Size: KB.

The Kaplan-Meier estimator of the survivorship function (or survival probability) S(t) = P(T>t) is: S^(t) = Q j:˝j t rj dj rj = Q j:˝j t 1 dj rj where ˝ 1;˝ K is the set of K distinct uncensored failure times observed in the sample d j is the number of failures at ˝ j r j is the number of individuals \at risk" right beforeFile Size: KB.

To cite this article: Md Hasinur Rahaman Khan & J. Ewart H. Shaw () Robust bias estimation for Kaplan–Meier survival estimator with jackknifing, Journal of Statistical Theory and Practice.

Let $\hat{F}_n$ be the Kaplan-Meier estimator of a distribution function F computed from randomly censored data. It is known that, under certain integrability assumptions on a function $\varphi$, the Kaplan-Meier integral $\int \varphi d \hat{F}_n$, when properly standardized, is asymptotically by: My data is neither normal or log-normal, and I've used non-parametric tests to analyze it so far.

The information in the environmental-research literature about the use of kaplan-meier, or ROS estimators for real left-censored data primarily only compares overall statistics (i.e. mean, median, std. dev) between these different estimator methods. The Kaplan-Meier procedure is a method of estimating time-to-event models in the presence of censored cases.

The Kaplan-Meier model is based on estimating conditional probabilities at each time point when an event occurs and taking the product limit of those probabilities to estimate the survival rate at each point in time. As well as core topics such as the Kaplan-Meier survival function estimator, log rank test, Cox model, etc, the second edition I have (there is now a third) includes coverage of additional topics such as accelerated failure time models, models for interval censored data, and sample size calculations for survival studies.

an illustration of how to generate a Kaplan-Meier curve in the presence of censoring. INTRODUCTION. InEdward L. Kaplan and Paul Meier collaborated to publish a seminal paper on how to deal with incomplete observations.

1 Subsequently, the Kaplan-Meier curves and estimates of survival data have become a familiar way of dealing with differing survival times (times-to-event), especially when not all the subjects continue in the by: Let F ^ n (x) denote the Kaplan-Meier product-limit estimate for the life distribution function F(x;θ 0) based on randomly censored data.

The M-estimator of θ 0 corresponding to a function ρ. Abstract For studying or reducing the bias of functionals of the Kaplan-Meier survival estimator, the jackknifing approach of Stute and Wang () is natural. We have studied the behavior of the jackknife estimate of bias under different configurations of the censoring level, sample size, and the censoring and survival time by: 2.

Until an instant before time=1, no events were observed (only the censored observation), so the survival estimate is 1. At time=1, 2 subjects out of the 18 still at risk observed the event, so the survival function S.) at time 1 is S(1) = 16/18 = The next failure occurs at time=2, with 16 still at risk, so S(2)=15/16 * 16/18 =.

LIFETEST to compute the Kaplan-Meier curve (), which is a nonparametric maximum likelihood estimate of the survivor function. The Kaplan-Meier plot (also called the product-limit survival plot) is a popular tool in medical, pharmaceutical, and life sciences research.

The Kaplan-Meier plot contains step.The problem is to estimate the survival probability P{D > t) with the censored data of N individuals. B. ESTIMATORS 1. Kaplan-Meier Estimator A non-parametric estimator of the distribution function for censored data is the Kaplan-Meier estimator(K.M.E.) which is often called the product limit estimator .Menu location: Analysis_Survival_Kaplan-Meier.

This function estimates survival rates and hazard from data that may be incomplete. The survival rate is expressed as the survivor function (S): where t is a time period known as the survival time, time to failure or time to event (such as death); e.g.

5 years in the context of 5 year survival rates.